import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.datasets import fetch_lfw_people
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.metrics import confusion_matrix
from sklearn.svm import SVC
from sklearn.decomposition import PCA
from sklearn.pipeline import make_pipeline
from time import time
import logging
from PIL import Image
from scipy import ndimage

# 导入至少60张不同人脸的图片，Ratio used to ‘0.5’ the each face picture
faces = fetch_lfw_people(min_faces_per_person=50)

# 打印程序进展
logging.basicConfig(level=logging.INFO, format='%(asctime)s %(message)s')
# 样本数1560，高125  宽94
n_samples, h, w = faces.images.shape
# 获取特征向量矩阵
X = faces.data
# 特征向量的维度(列数)或者称特征点的个数
n_features = X.shape[1]

# 返回每一组的特征标记
y = faces.target
target_names = faces.target_names
# 返回多少类(多少行)，也就是多少个人进行人脸识别
n_classes = target_names.shape[0]
# # 数据集的拆分
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)
# # 准备开始数据集的降维  并计算时间
print("Extracting the top %d eigenfaces from %d faces" % (150, X_train.shape[0]))
t0 = time()
pca = PCA(n_components=150, whiten=True).fit(X_train)
print("done in %0.3fs" % (time() - t0))
#
# # 提取特征值
eigenfaces = pca.components_.reshape((150, h, w))
#
print("Projecting the input data on the eigenfaces orthonormal basis")
t0 = time()
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
print("done in %0.3fs" % (time() - t0))

#
# # Train a SVM classification model_path
#
print("Fitting the classifier to the training set")
t0 = time()
param_grid = {'C': [1e3, 5e3, 1e4, 5e4, 1e5], 'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1], }
# clf = GridSearchCV(SVC(kernel='rbf', class_weight='auto'), param_grid)
clf = GridSearchCV(SVC(kernel='rbf'), param_grid)
clf = clf.fit(X_train_pca, y_train)
print("done in %0.3fs" % (time() - t0))
print("Best estimator found by grid search:")
print(clf.best_estimator_)
#
# # Quantitative evaluation of the model_path quality on the test set
#
print("Predicting people's names on the test set")
t0 = time()
y_pred = clf.predict(X_test_pca)
print("done in %0.3fs" % (time() - t0))

print(classification_report(y_test, y_pred, target_names=target_names))
matrix = confusion_matrix(y_test, y_pred, labels=range(n_classes))
print(matrix)


# 热度图的绘制  用来表现混淆矩阵
colormap = plt.cm.viridis
sns.heatmap(matrix,linewidths=0.1,vmax=10,square=True,cmap=colormap,linecolor='white',annot=True)
plt.show()











#
def plot_gallery(images, titles, h, w, n_row=3, n_col=4):
    """Helper function to plot a gallery of portraits"""
    plt.figure(figsize=(1.8 * n_col, 2.4 * n_row))
    plt.subplots_adjust(bottom=0, left=.01, right=.99, top=.90, hspace=.35)
    for i in range(n_row * n_col):
        plt.subplot(n_row, n_col, i + 1)
        plt.imshow(images[i].reshape((h, w)), cmap=plt.cm.gray)
        plt.title(titles[i], size=12)
        plt.xticks(())
        plt.yticks(())
#
#
# # plot the result of the prediction on a portion of the test set
#
def title(y_pred, y_test, target_names, i):
    pred_name = target_names[y_pred[i]].rsplit(' ', 1)[-1]
    true_name = target_names[y_test[i]].rsplit(' ', 1)[-1]
    return 'predicted: %s\ntrue:      %s' % (pred_name, true_name)
#
#
prediction_titles = [title(y_pred, y_test, target_names, i)
                     for i in range(y_pred.shape[0])]
#
plot_gallery(X_test, prediction_titles, h, w)
plt.show()
#
# plot the gallery of the most significative eigenfaces
#
eigenface_titles = ["eigenface %d" % i for i in range(eigenfaces.shape[0])]
plot_gallery(eigenfaces, eigenface_titles, h, w)
#
plt.show()



# https://blog.csdn.net/jasonzhoujx/article/details/81905923
# https://blog.csdn.net/tanghong1996/article/details/81217293
# 本次学习和测试暂时完结 2019年5月9日